Real-World Research (RWR) – Simple Studies that Present Unique Challenges and Deliver Important Outcomes

What is Real-World Research?

Real-World Research (RWR) involves collecting patient-related data in a real-world environment (real-world data – RWD) and generating clinical evidence (real-world evidence – RWE) of the value and potential benefits, or risks, of medicinal products through analysis.

Examples of real-world research include non-interventional studies, observational studies, registries and pragmatic clinical trials.

Why is Real-World Research Important?

Real-world research, inclusive of pragmatic clinical trials, can be used to generate real-world evidence (RWE) to support drug (and vaccine) development, registration, and reimbursement, and can help to address the ‘limitations’ of traditional clinical trials by providing information about what actually happens in the ‘real world’ (e.g., comparative effectiveness of a drug, lack of effectiveness of a vaccine, poly pharmacy safety signals, rare adverse events) versus what we thought would happen based on the experimental results from traditional clinical trials.

RWE Can be Used to Support Drug Development

Real-world evidence (RWE) can support drug development through a variety of ways, covering pre-marketing clinical development and post-marketing evaluation (see Figure 1). Scenarios where real-world evidence can support drug development and regulatory decisions:

  • Guiding clinical trial design

  • Identify the target population

  • Treatment of rare diseases

  • Revision of indications or drug combination labeling

  • Post-marketing evaluation and surveillance

RWD Can be Used to Improve the Efficiency of Clinical Trials

Real-world data (RWD) can be used to improve the efficiency of clinical trials, even if not used to generate RWE regarding product effectiveness. For example, RWD can help with:

  • Generating hypotheses for testing in randomized controlled trials

  • Identifying drug development tools (including biomarker identification)

  • Assessing trial feasibility by examining the impact of planned inclusion/exclusion criteria in the relevant population, both within a geographical area or at a particular trial site

  • Informing prior probability distributions in Bayesian statistical models

  • Identifying prognostic indicators or patient baseline characteristics for enrichment or stratification

  • Assembling geographically distributed research cohorts (e.g., in drug development for rare diseases or targeted therapeutics)

Figure 1 – Source of Real-World Data (RWD) and Uses of Real-World Evidence (RWE)

RWD ‘Cheat Sheet’ of Quality Factors needed to Ensure ‘Acceptability’ of RWE to Regulators

Key factors to determine whether the quality of real-world data can support drug development include  (but are not limited to):

  • A clear process and qualified personnel for data collection

  • Common defining framework, i.e., a common data dictionary, is used

  • Common time frame for key data points collection is followed

  • Study plan, protocol and/or analysis plan related to the collection of real-world data have been established

  • Technical approach used for data element capture, including integration of data from various sources, data records of drug use, links to claims data etc., is adequate

  • Patient recruitment minimizes bias and reflects the true target population

  • Data entry and transfer are useable and timely

  • Adequate and necessary patient protection measures such as patient privacy protection and regulatory compliance with informed consent are in place

We Need to Approach Real-World Research with a Different Mindset to Traditional Clinical Trials


The majority of real-world research study designs are not clinical trials. This is where the regulatory and operational complications start.

OK, fair enough…So HOW should we approach Real-World Research?

Start from first principles. Determine which regulatory ‘framework’ fits your research study…is it a clinical trial?  If not, what is it? And then consider…are there local nuances that impact whether the study can be run (e.g., biosampling in Germany) or impact the startup requirements (e.g., study classification in Spain)

Let this inform the regulatory requirements and operational considerations as you build your project playbook from study start-up through to close-out

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